Modeling - Revision history

Hdridder at 12:35, 24 January 2020

2020-01-24T12:35:37Z

← Older revision		Revision as of 12:35, 24 January 2020
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	[[Category:AI]]		[[Category:AI]]
	Mostly based on [http://greenteapress.com/~~ModSimPy~~/~~ModSimPy~~.pdf this paper] that comes with its own modsim library.		Mostly based on an older version of [http://greenteapress.com/modsimpy/ModSimPy3.pdf this paper] that comes with its own modsim library.

	* A model depicts a system.		* A model depicts a system.

Hdridder at 19:13, 6 April 2019

2019-04-06T19:13:41Z

← Older revision		Revision as of 19:13, 6 April 2019
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			[[Category:AI]]
	Mostly based on [http://greenteapress.com/ModSimPy/ModSimPy.pdf this paper] that comes with its own modsim library.		Mostly based on [http://greenteapress.com/ModSimPy/ModSimPy.pdf this paper] that comes with its own modsim library.

Hdridder: /* Naming conventions */

2019-03-06T18:52:37Z

Naming conventions

← Older revision		Revision as of 18:52, 6 March 2019
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	:First time-stamp		:First time-stamp
	;system.p_0		;system.p_0
	:~~Status~~ of the system at t_0		:State of the system at t_0 (a Pandas series)
	;system.t_end		;system.t_end
	:Last time-stamp		:Last time-stamp

Hdridder: /* Naming conventions */

2019-03-03T15:16:22Z

Naming conventions

← Older revision		Revision as of 15:16, 3 March 2019
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	;system.alpha		;system.alpha
	:Variable to store the net change.		:Variable to store the net change.
			;system.t_0
			:First time-stamp
			;system.p_0
			:Status of the system at t_0
			;system.t_end
			:Last time-stamp

Hdridder: /* Naming conventions */

2019-03-03T15:09:45Z

Naming conventions

← Older revision		Revision as of 15:09, 3 March 2019
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	;system		;system
	:Pandas Series to store parameters of how the model behaves		:[[Pandas#Series]] to store parameters of how the model behaves
	;alpha		;system.alpha
	:Variable ~~(in system)~~ to store the net change.		:Variable to store the net change.

Hdridder at 20:14, 31 December 2018

2018-12-31T20:14:59Z

← Older revision		Revision as of 20:14, 31 December 2018
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	* The state of the system attributes is stored in a [[Pandas#Series]]		* The state of the system attributes is stored in a [[Pandas#Series]]
	* Results of how the system attributes change over time are stored in Pandas Series too (1 Series per attribute).		* Results of how the system attributes change over time are stored in Pandas Series too (1 Series per attribute).
	* Parameters that determine how the system attributes change are stored ~~in a [[Python:DataTypes#Dictionary_or_dict\|dictionary]] or also~~ in a pandas series.		* Parameters that determine how the system attributes change are also stored in a pandas series.
	* The functions that change the system attributes take the system parameters as parameter. This way you can play with the parameter values to get the best result.		* The functions that change the system attributes take the system parameters as parameter. This way you can play with the parameter values to get the best result.
	** [[Numpy#linspace]] provides an equally distributed range of values so you can play easily between the limits you set.		** [[Numpy#linspace]] provides an equally distributed range of values so you can play easily between the limits you set.
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	funcBresults[a] = functionBcall(bla,bla)		funcBresults[a] = functionBcall(bla,bla)
	</syntaxhighlight>		</syntaxhighlight>

			=Naming conventions=

			;system
			:Pandas Series to store parameters of how the model behaves
			;alpha
			:Variable (in system) to store the net change.

Hdridder at 13:37, 25 December 2018

2018-12-25T13:37:59Z

← Older revision		Revision as of 13:37, 25 December 2018
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	* Parameters that determine how the system attributes change are stored in a [[Python:DataTypes#Dictionary_or_dict\|dictionary]] or also in a pandas series.		* Parameters that determine how the system attributes change are stored in a [[Python:DataTypes#Dictionary_or_dict\|dictionary]] or also in a pandas series.
	* The functions that change the system attributes take the system parameters as parameter. This way you can play with the parameter values to get the best result.		* The functions that change the system attributes take the system parameters as parameter. This way you can play with the parameter values to get the best result.
	* [[Numpy#linspace]] provides an equally distributed range of values so you can play easily between the limits you set.		** [[Numpy#linspace]] provides an equally distributed range of values so you can play easily between the limits you set.
	* The quality of the model can be judged by calculating the relative error of the [[Approximation]]		* The quality of the model can be judged by calculating the relative error of the [[Approximation]]

Hdridder at 13:36, 25 December 2018

2018-12-25T13:36:42Z

← Older revision		Revision as of 13:36, 25 December 2018
Line 1:		Line 1:
	Mostly based on [http://greenteapress.com/ModSimPy/ModSimPy.pdf this paper] that comes with its own modsim library.		Mostly based on [http://greenteapress.com/ModSimPy/ModSimPy.pdf this paper] that comes with its own modsim library.

	~~=Eyeopeners=~~		* A model depicts a system.
	* ~~Store results~~ in a [[~~Python:DataTypes~~#~~List\|list~~]]		* The state of the system attributes is stored in a [[Pandas#Series]]
	~~<syntaxhighlight lang=python>~~		* Results of how the system attributes change over time are stored in Pandas Series too (1 Series per attribute).
	~~for a~~ in ~~range~~(~~100~~):		* Parameters that determine how the system attributes change are stored in a [[Python:DataTypes#Dictionary_or_dict\|dictionary]] or also in a pandas series.
	~~funcAresults~~[a] ~~= functionAcall(bla,bla)~~		* The functions that change the system attributes take the system parameters as parameter. This way you can play with the parameter values to get the best result.
	~~funcBresults[~~a~~] = functionBcall(bla,bla)~~		* [[Numpy#linspace]] provides an equally distributed range of values so you can play easily between the limits you set.
	~~</syntaxhighlight>~~		* The quality of the model can be judged by calculating the relative error of the [[Approximation]]
	~~: ModSimPy is using Series from~~ [[~~Pandas~~]] ~~to store results. This adds handy functions like~~.
	* The ~~state~~ of the model ~~is stored in a Pandas Series too.~~		=Other remarks=
	* Put other interesting metrics in the state object too.
	* If you use randomness in a function, get the mean values of several runs.		* If you use randomness in a function, get the mean values of several runs.
	* Check the effect of different parameter values using [[Numpy]] [[Numpy#linspace \| linspace]].
			=Example code=
			[[Numpy]] [[Numpy#linspace \| linspace]]
	<syntaxhighlight lang=python>		<syntaxhighlight lang=python>
	funcAresults = pd.Series([])		funcAresults = pd.Series([])
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	print(funcAresults)		print(funcAresults)
	</syntaxhighlight>		</syntaxhighlight>
	* The [~~https://en.wikipedia.org/wiki/Approximation_error Approximation error~~] ([~~https://nl.wikipedia.org/wiki/Benaderingsfout benaderingsfout~~]~~) is the difference between a calculated value and the real value. By dividing it by the real value you get the relative error. A bit relative error indicates bad calculation.~~
	~~The largest relative error between two arrays as percentage:~~		Store results in a [[Pandas]] [[Pandas#Series\|Series]]
	<syntaxhighlight lang=python>		<syntaxhighlight lang=python>
	~~max~~(~~abs~~(~~calculated - real~~) ~~/ real~~ ) * 100		for a in range(100):
			funcAresults[a] = functionAcall(bla,bla)
			funcBresults[a] = functionBcall(bla,bla)
	</syntaxhighlight>		</syntaxhighlight>

Hdridder at 20:10, 24 December 2018

2018-12-24T20:10:34Z

← Older revision		Revision as of 20:10, 24 December 2018
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	funcAresults[a] = functionAcall(p1,p2)		funcAresults[a] = functionAcall(p1,p2)
	print(funcAresults)		print(funcAresults)
			</syntaxhighlight>
			* The [https://en.wikipedia.org/wiki/Approximation_error Approximation error] ([https://nl.wikipedia.org/wiki/Benaderingsfout benaderingsfout]) is the difference between a calculated value and the real value. By dividing it by the real value you get the relative error. A bit relative error indicates bad calculation.
			The largest relative error between two arrays as percentage:
			<syntaxhighlight lang=python>
			max(abs(calculated - real) / real ) * 100
	</syntaxhighlight>		</syntaxhighlight>

Hdridder at 21:54, 19 December 2018

2018-12-19T21:54:13Z

← Older revision		Revision as of 21:54, 19 December 2018
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	* The state of the model is stored in a Pandas Series too.		* The state of the model is stored in a Pandas Series too.
	* Put other interesting metrics in the state object too.		* Put other interesting metrics in the state object too.
			* If you use randomness in a function, get the mean values of several runs.
	* Check the effect of different parameter values using [[Numpy]] [[Numpy#linspace \| linspace]].		* Check the effect of different parameter values using [[Numpy]] [[Numpy#linspace \| linspace]].
	<syntaxhighlight lang=python>		<syntaxhighlight lang=python>